





'''simple docstring'''






import argparse
import json
import os

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType
from accelerate.utils.deepspeed import DummyOptim, DummyScheduler


lowerCAmelCase									=   16
lowerCAmelCase									=   32






def   __A    (				a_    :	Accelerator   ,a_    :	int = 1_6   ,a_    :	str = "bert-base-cased"						):
		lowerCAmelCase  :     List[str]       						= AutoTokenizer.from_pretrained(a_						)
		lowerCAmelCase  :     Union[str, Any]       						= load_dataset("glue"   ,"mrpc"						)

		def tokenize_function(a_    :	List[str]						):
				# max_length=None => use the model max length (it's actually the default)
				lowerCAmelCase  :     List[Any]       						= tokenizer(examples["sentence1"]   ,examples["sentence2"]   ,truncation=a_   ,max_length=a_						)
				return outputs

		# Apply the method we just defined to all the examples in all the splits of the dataset
		lowerCAmelCase  :     Tuple       						= datasets.map(
		    a_   ,batched=a_   ,remove_columns=["idx", "sentence1", "sentence2"]   ,load_from_cache_file=a_						)

		# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
		# transformers library
		lowerCAmelCase  :     List[Any]       						= tokenized_datasets.rename_column("label"   ,"labels"						)

		def collate_fn(a_    :	Optional[Any]						):
				# On TPU it's best to pad everything to the same length or training will be very slow.
				if accelerator.distributed_type == DistributedType.TPU:
						return tokenizer.pad(a_   ,padding="max_length"   ,max_length=1_2_8   ,return_tensors="pt"						)
				return tokenizer.pad(a_   ,padding="longest"   ,return_tensors="pt"						)

		# Instantiate dataloaders.
		lowerCAmelCase  :     Optional[int]       						= DataLoader(
		    tokenized_datasets["train"]   ,shuffle=a_   ,collate_fn=a_   ,batch_size=a_						)
		lowerCAmelCase  :     Dict       						= DataLoader(
		    tokenized_datasets["validation"]   ,shuffle=a_   ,collate_fn=a_   ,batch_size=a_						)

		return train_dataloader, eval_dataloader






def   __A    (				a_    :	Dict   ,a_    :	int						):
		# Initialize accelerator
		lowerCAmelCase  :     int       						= Accelerator()

		# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
		lowerCAmelCase  :     Tuple       						= config["lr"]
		lowerCAmelCase  :     Tuple       						= int(config["num_epochs"]						)
		lowerCAmelCase  :     Dict       						= int(config["seed"]						)
		lowerCAmelCase  :     str       						= int(config["batch_size"]						)
		lowerCAmelCase  :     Optional[Any]       						= args.model_name_or_path

		set_seed(a_						)
		lowerCAmelCase						,		lowerCAmelCase  :     List[str]       						= get_dataloaders(a_   ,a_   ,a_						)

		# Instantiate the model (we build the model here so that the seed also control new weights initialization)
		lowerCAmelCase  :     Optional[Any]       						= AutoModelForSequenceClassification.from_pretrained(a_   ,return_dict=a_						)

		# Instantiate optimizer
		lowerCAmelCase  :     Dict       						= (
		    AdamW
		    if accelerator.state.deepspeed_plugin is None
		    or "optimizer" not in accelerator.state.deepspeed_plugin.deepspeed_config
		    else DummyOptim
		)
		lowerCAmelCase  :     List[Any]       						= optimizer_cls(params=model.parameters()   ,lr=a_						)

		if accelerator.state.deepspeed_plugin is not None:
				lowerCAmelCase  :     List[Any]       						= accelerator.state.deepspeed_plugin.deepspeed_config[
				    "gradient_accumulation_steps"
				]
		else:
				lowerCAmelCase  :     List[Any]       						= 1
		lowerCAmelCase  :     str       						= (len(a_						) * num_epochs) // gradient_accumulation_steps

		# Instantiate scheduler
		if (
		    accelerator.state.deepspeed_plugin is None
		    or "scheduler" not in accelerator.state.deepspeed_plugin.deepspeed_config
		):
				lowerCAmelCase  :     Dict       						= get_linear_schedule_with_warmup(
				    optimizer=a_   ,num_warmup_steps=0   ,num_training_steps=a_   ,)
		else:
				lowerCAmelCase  :     Union[str, Any]       						= DummyScheduler(a_   ,total_num_steps=a_   ,warmup_num_steps=0						)

		# Prepare everything
		# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
		# prepare method.
		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= accelerator.prepare(
		    a_   ,a_   ,a_   ,a_   ,a_						)

		# We need to keep track of how many total steps we have iterated over
		lowerCAmelCase  :     Optional[int]       						= 0
		# We also need to keep track of the stating epoch so files are named properly
		lowerCAmelCase  :     List[Any]       						= 0

		# Now we train the model
		lowerCAmelCase  :     Union[str, Any]       						= evaluate.load("glue"   ,"mrpc"						)
		lowerCAmelCase  :     List[Any]       						= 0
		lowerCAmelCase  :     Any       						= {}
		for epoch in range(a_   ,a_						):
				model.train()
				for step, batch in enumerate(a_						):
						lowerCAmelCase  :     Tuple       						= model(**a_						)
						lowerCAmelCase  :     List[str]       						= outputs.loss
						lowerCAmelCase  :     Tuple       						= loss / gradient_accumulation_steps
						accelerator.backward(a_						)
						if step % gradient_accumulation_steps == 0:
								optimizer.step()
								lr_scheduler.step()
								optimizer.zero_grad()

						overall_step += 1

				model.eval()
				lowerCAmelCase  :     Optional[int]       						= 0
				for step, batch in enumerate(a_						):
						# We could avoid this line since we set the accelerator with `device_placement=True`.
						batch.to(accelerator.device						)
						with torch.no_grad():
								lowerCAmelCase  :     str       						= model(**a_						)
						lowerCAmelCase  :     Union[str, Any]       						= outputs.logits.argmax(dim=-1						)
						# It is slightly faster to call this once, than multiple times
						lowerCAmelCase						,		lowerCAmelCase  :     Optional[Any]       						= accelerator.gather(
						    (predictions, batch["labels"])						)  # If we are in a multiprocess environment, the last batch has duplicates
						if accelerator.use_distributed:
								if step == len(a_						) - 1:
										lowerCAmelCase  :     Optional[int]       						= predictions[: len(eval_dataloader.dataset						) - samples_seen]
										lowerCAmelCase  :     Optional[int]       						= references[: len(eval_dataloader.dataset						) - samples_seen]
								else:
										samples_seen += references.shape[0]
						metric.add_batch(
						    predictions=a_   ,references=a_   ,)

				lowerCAmelCase  :     Optional[int]       						= metric.compute()
				# Use accelerator.print to print only on the main process.
				accelerator.print(f'''epoch {epoch}:'''   ,a_						)
				lowerCAmelCase  :     int       						= eval_metric["accuracy"]

				if best_performance < eval_metric["accuracy"]:
						lowerCAmelCase  :     Optional[Any]       						= eval_metric["accuracy"]

		if args.performance_lower_bound is not None:
				assert (
				    args.performance_lower_bound <= best_performance
				), f'''Best performance metric {best_performance} is lower than the lower bound {args.performance_lower_bound}'''

		accelerator.wait_for_everyone()
		if accelerator.is_main_process:
				with open(os.path.join(args.output_dir   ,"all_results.json"						)   ,"w"						) as f:
						json.dump(a_   ,a_						)






def   __A    (				):
		lowerCAmelCase  :     Dict       						= argparse.ArgumentParser(description="Simple example of training script tracking peak GPU memory usage."						)
		parser.add_argument(
		    "--model_name_or_path"   ,type=a_   ,default="bert-base-cased"   ,help="Path to pretrained model or model identifier from huggingface.co/models."   ,required=a_   ,)
		parser.add_argument(
		    "--output_dir"   ,type=a_   ,default="."   ,help="Optional save directory where all checkpoint folders will be stored. Default is the current working directory."   ,)
		parser.add_argument(
		    "--performance_lower_bound"   ,type=a_   ,default=a_   ,help="Optional lower bound for the performance metric. If set, the training will throw error when the performance metric drops below this value."   ,)
		parser.add_argument(
		    "--num_epochs"   ,type=a_   ,default=3   ,help="Number of train epochs."   ,)
		lowerCAmelCase  :     Union[str, Any]       						= parser.parse_args()
		lowerCAmelCase  :     str       						= {"lr": 2e-5, "num_epochs": args.num_epochs, "seed": 4_2, "batch_size": 1_6}
		training_function(a_   ,a_						)


if __name__ == "__main__":
					main()










'''simple docstring'''






import json
import os
import shutil
import warnings
from argparse import ArgumentParser, Namespace
from pathlib import Path
from typing import List

from ..utils import logging
from . import BaseTransformersCLICommand


try:
					from cookiecutter.main import cookiecutter

					lowerCAmelCase									=   True
except ImportError:
					lowerCAmelCase									=   False

lowerCAmelCase									=   logging.get_logger(__name__)  # pylint: disable=invalid-name






def   __A    (				a_    :	Namespace						):
		return AddNewModelCommand(args.testing   ,args.testing_file   ,path=args.path						)

class 		lowerCamelCase	(   _A							):



	@staticmethod
	def 	_lowerCamelCase     ( a_						):
			lowerCAmelCase  :     Optional[int]       						= parser.add_parser("add-new-model"						)
			add_new_model_parser.add_argument("--testing"		,   action="store_true"		,   help="If in testing mode."						)
			add_new_model_parser.add_argument("--testing_file"		,   type=a_		,   help="Configuration file on which to run."						)
			add_new_model_parser.add_argument(
			    "--path"		,   type=a_		,   help="Path to cookiecutter. Should only be used for testing purposes."						)
			add_new_model_parser.set_defaults(func=a_						)



	def __init__( self		,   a_		,   a_		,   a_=None		,   *a_						):
			lowerCAmelCase  :     Any       						= testing
			lowerCAmelCase  :     str       						= testing_file
			lowerCAmelCase  :     Optional[int]       						= path





	def 	_lowerCamelCase     ( self						):
			warnings.warn(
			    "The command `transformers-cli add-new-model` is deprecated and will be removed in v5 of Transformers. "
			    "It is not actively maintained anymore, so might give a result that won't pass all tests and quality "
			    "checks, you should use `transformers-cli add-new-model-like` instead."						)
			if not _has_cookiecutter:
					raise ImportError(
					    "Model creation dependencies are required to use the `add_new_model` command. Install them by running "
					    "the following at the root of your `transformers` clone:\n\n\t$ pip install -e .[modelcreation]\n"						)
			# Ensure that there is no other `cookiecutter-template-xxx` directory in the current working directory
			lowerCAmelCase  :     Optional[Any]       						= [directory for directory in os.listdir() if "cookiecutter-template-" == directory[:22]]
			if len(a_						) > 0:
					raise ValueError(
					    "Several directories starting with `cookiecutter-template-` in current working directory. "
					    "Please clean your directory by removing all folders starting with `cookiecutter-template-` or "
					    "change your working directory."						)

			lowerCAmelCase  :     List[str]       						= (
			    Path(a_						).parent.parent.parent.parent if self._path is None else Path(self._path						).parent.parent
			)
			lowerCAmelCase  :     str       						= path_to_transformer_root / "templates" / "adding_a_new_model"

			# Execute cookiecutter
			if not self._testing:
					cookiecutter(str(a_						)						)
			else:
					with open(self._testing_file		,   "r"						) as configuration_file:
							lowerCAmelCase  :     Dict       						= json.load(a_						)

					cookiecutter(
					    str(path_to_cookiecutter if self._path is None else self._path						)		,   no_input=a_		,   extra_context=a_		,   )

			lowerCAmelCase  :     Optional[Any]       						= [directory for directory in os.listdir() if "cookiecutter-template-" in directory[:22]][0]

			# Retrieve configuration
			with open(directory + "/configuration.json"		,   "r"						) as configuration_file:
					lowerCAmelCase  :     str       						= json.load(a_						)

			lowerCAmelCase  :     List[str]       						= configuration["lowercase_modelname"]
			lowerCAmelCase  :     List[Any]       						= configuration["generate_tensorflow_pytorch_and_flax"]
			os.remove(F'''{directory}/configuration.json'''						)

			lowerCAmelCase  :     Optional[int]       						= "PyTorch" in generate_tensorflow_pytorch_and_flax
			lowerCAmelCase  :     Optional[int]       						= "TensorFlow" in generate_tensorflow_pytorch_and_flax
			lowerCAmelCase  :     Optional[Any]       						= "Flax" in generate_tensorflow_pytorch_and_flax

			lowerCAmelCase  :     List[str]       						= F'''{path_to_transformer_root}/src/transformers/models/{lowercase_model_name}'''
			os.makedirs(a_		,   exist_ok=a_						)
			os.makedirs(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}'''		,   exist_ok=a_						)

			# Tests require submodules as they have parent imports
			with open(F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/__init__.py'''		,   "w"						):
					pass

			shutil.move(
			    F'''{directory}/__init__.py'''		,   F'''{model_dir}/__init__.py'''		,   )
			shutil.move(
			    F'''{directory}/configuration_{lowercase_model_name}.py'''		,   F'''{model_dir}/configuration_{lowercase_model_name}.py'''		,   )

			def remove_copy_lines(a_						):
					with open(a_		,   "r"						) as f:
							lowerCAmelCase  :     Dict       						= f.readlines()
					with open(a_		,   "w"						) as f:
							for line in lines:
									if "# Copied from transformers." not in line:
											f.write(a_						)

			if output_pytorch:
					if not self._testing:
							remove_copy_lines(F'''{directory}/modeling_{lowercase_model_name}.py'''						)

					shutil.move(
					    F'''{directory}/modeling_{lowercase_model_name}.py'''		,   F'''{model_dir}/modeling_{lowercase_model_name}.py'''		,   )

					shutil.move(
					    F'''{directory}/test_modeling_{lowercase_model_name}.py'''		,   F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_{lowercase_model_name}.py'''		,   )
			else:
					os.remove(F'''{directory}/modeling_{lowercase_model_name}.py'''						)
					os.remove(F'''{directory}/test_modeling_{lowercase_model_name}.py'''						)

			if output_tensorflow:
					if not self._testing:
							remove_copy_lines(F'''{directory}/modeling_tf_{lowercase_model_name}.py'''						)

					shutil.move(
					    F'''{directory}/modeling_tf_{lowercase_model_name}.py'''		,   F'''{model_dir}/modeling_tf_{lowercase_model_name}.py'''		,   )

					shutil.move(
					    F'''{directory}/test_modeling_tf_{lowercase_model_name}.py'''		,   F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_tf_{lowercase_model_name}.py'''		,   )
			else:
					os.remove(F'''{directory}/modeling_tf_{lowercase_model_name}.py'''						)
					os.remove(F'''{directory}/test_modeling_tf_{lowercase_model_name}.py'''						)

			if output_flax:
					if not self._testing:
							remove_copy_lines(F'''{directory}/modeling_flax_{lowercase_model_name}.py'''						)

					shutil.move(
					    F'''{directory}/modeling_flax_{lowercase_model_name}.py'''		,   F'''{model_dir}/modeling_flax_{lowercase_model_name}.py'''		,   )

					shutil.move(
					    F'''{directory}/test_modeling_flax_{lowercase_model_name}.py'''		,   F'''{path_to_transformer_root}/tests/models/{lowercase_model_name}/test_modeling_flax_{lowercase_model_name}.py'''		,   )
			else:
					os.remove(F'''{directory}/modeling_flax_{lowercase_model_name}.py'''						)
					os.remove(F'''{directory}/test_modeling_flax_{lowercase_model_name}.py'''						)

			shutil.move(
			    F'''{directory}/{lowercase_model_name}.md'''		,   F'''{path_to_transformer_root}/docs/source/en/model_doc/{lowercase_model_name}.md'''		,   )

			shutil.move(
			    F'''{directory}/tokenization_{lowercase_model_name}.py'''		,   F'''{model_dir}/tokenization_{lowercase_model_name}.py'''		,   )

			shutil.move(
			    F'''{directory}/tokenization_fast_{lowercase_model_name}.py'''		,   F'''{model_dir}/tokenization_{lowercase_model_name}_fast.py'''		,   )

			from os import fdopen, remove
			from shutil import copymode, move
			from tempfile import mkstemp

			def replace(a_		,   a_		,   a_						):
					# Create temp file
					lowerCAmelCase						,		lowerCAmelCase  :     Tuple       						= mkstemp()
					lowerCAmelCase  :     int       						= False
					with fdopen(a_		,   "w"						) as new_file:
							with open(a_						) as old_file:
									for line in old_file:
											new_file.write(a_						)
											if line_to_copy_below in line:
													lowerCAmelCase  :     Optional[int]       						= True
													for line_to_copy in lines_to_copy:
															new_file.write(a_						)

					if not line_found:
							raise ValueError(F'''Line {line_to_copy_below} was not found in file.'''						)

					# Copy the file permissions from the old file to the new file
					copymode(a_		,   a_						)
					# Remove original file
					remove(a_						)
					# Move new file
					move(a_		,   a_						)

			def skip_units(a_						):
					return (
					    ("generating PyTorch" in line and not output_pytorch)
					    or ("generating TensorFlow" in line and not output_tensorflow)
					    or ("generating Flax" in line and not output_flax)
					)

			def replace_in_files(a_						):
					with open(a_						) as datafile:
							lowerCAmelCase  :     Dict       						= []
							lowerCAmelCase  :     Optional[int]       						= False
							lowerCAmelCase  :     List[Any]       						= False
							for line in datafile:
									if "# To replace in: " in line and "##" not in line:
											lowerCAmelCase  :     Optional[int]       						= line.split("\""						)[1]
											lowerCAmelCase  :     Tuple       						= skip_units(a_						)
									elif "# Below: " in line and "##" not in line:
											lowerCAmelCase  :     Any       						= line.split("\""						)[1]
											lowerCAmelCase  :     List[str]       						= skip_units(a_						)
									elif "# End." in line and "##" not in line:
											if not skip_file and not skip_snippet:
													replace(a_		,   a_		,   a_						)

											lowerCAmelCase  :     List[str]       						= []
									elif "# Replace with" in line and "##" not in line:
											lowerCAmelCase  :     Any       						= []
									elif "##" not in line:
											lines_to_copy.append(a_						)

					remove(a_						)

			replace_in_files(F'''{directory}/to_replace_{lowercase_model_name}.py'''						)
			os.rmdir(a_						)



